A major limitation of ANI is the necessity to train dedicated neural networks for each atom type and the usage of handcrafted features like AEV. Graph neural networks provide a more general approach for the prediction of atomic energies and forces. A variety of neural networks has been designed to leverage symmetries of tasks on molecular graphs, with equivariance and invariance as their foundational principles.[1]}, [2]} Equivariance for translation and rotation in 3D (symmetries described by the SE(3) group) were first investigated by Cohen et al. [3]} , Esteves et al. [4]}, and Kondor et al. [5]} and were transferred to 3D graphs of molecules by Anderson et al. [6]}, Batzner et al. [7]}, Finzi et al. [8]}, and Fuchs et al. [9]}. Recently, Dym et al. [10]} has proven the universality of the joined group of translations, rotations, and permutations of the SE(3)-Transformer [9]}.
